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tf.keras.optimizers.schedules.PolynomialDecay

TensorFlow 1 version View source on GitHub

Class PolynomialDecay

A LearningRateSchedule that uses a polynomial decay schedule.

Inherits From: LearningRateSchedule

Aliases:

__init__

View source

__init__(
    initial_learning_rate,
    decay_steps,
    end_learning_rate=0.0001,
    power=1.0,
    cycle=False,
    name=None
)

Applies a polynomial decay to the learning rate.

It is commonly observed that a monotonically decreasing learning rate, whose degree of change is carefully chosen, results in a better performing model. This schedule applies a polynomial decay function to an optimizer step, given a provided initial_learning_rate, to reach an end_learning_rate in the given decay_steps.

It requires a step value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The schedule is a 1-arg callable that produces a decayed learning rate when passed the current optimizer step. This can be useful for changing the learning rate value across different invocations of optimizer functions. It is computed as:

def decayed_learning_rate(step):
  step = min(step, decay_steps)
  return ((initial_learning_rate - end_learning_rate) *
          (1 - step / decay_steps) ^ (power)
         ) + end_learning_rate

If cycle is True then a multiple of decay_steps is used, the first one that is bigger than step.

def decayed_learning_rate(step):
  decay_steps = decay_steps * ceil(step / decay_steps)
  return ((initial_learning_rate - end_learning_rate) *
          (1 - step / decay_steps) ^ (power)
         ) + end_learning_rate

You can pass this schedule directly into a tf.keras.optimizers.Optimizer as the learning rate. Example: Fit a model while decaying from 0.1 to 0.01 in 10000 steps using sqrt (i.e. power=0.5):

...
starter_learning_rate = 0.1
end_learning_rate = 0.01
decay_steps = 10000
learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(
    starter_learning_rate,
    decay_steps,
    end_learning_rate,
    power=0.5)

model.compile(optimizer=tf.keras.optimizers.SGD(
                  learning_rate=learning_rate_fn),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(data, labels, epochs=5)

The learning rate schedule is also serializable and deserializable using tf.keras.optimizers.schedules.serialize and tf.keras.optimizers.schedules.deserialize.

Args:

  • initial_learning_rate: A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
  • decay_steps: A scalar int32 or int64 Tensor or a Python number. Must be positive. See the decay computation above.
  • end_learning_rate: A scalar float32 or float64 Tensor or a Python number. The minimal end learning rate.
  • power: A scalar float32 or float64 Tensor or a Python number. The power of the polynomial. Defaults to linear, 1.0.
  • cycle: A boolean, whether or not it should cycle beyond decay_steps.
  • name: String. Optional name of the operation. Defaults to 'PolynomialDecay'.

Returns:

A 1-arg callable learning rate schedule that takes the current optimizer step and outputs the decayed learning rate, a scalar Tensor of the same type as initial_learning_rate.

Methods

__call__

View source

__call__(step)

Call self as a function.

from_config

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from_config(
    cls,
    config
)

Instantiates a LearningRateSchedule from its config.

Args:

  • config: Output of get_config().

Returns:

A LearningRateSchedule instance.

get_config

View source

get_config()